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Irregular Attendance of University Students at Class and its Relation to their Academic Achievement

Tribhuvan University Journal

The paper intends to examine the irregular attendance of students in their class and its relation to their academic achievement in five central campuses of Mid- Western University. This study followed descriptive study based on quantitative and qualitative data. Quantitative data were obtained from 172 students selected by non-proportional stratified sampling. Qualitative data were obtained from the campus chiefs, heads of instruction committees and teachers of the central campuses selected purposively. A mixed questionnaire was employed for quantitative data and open ended questionnaire was used to collect qualitative data. The study showed that near about half portion of respondents responded that they were sometimes irregular in their class. Few students (4.45%) who were never irregular belonged to the category of having knowledge of irregular attendance. Higher portion (29.57%) of the male students were always irregular than the female students. The high portion of Master's...

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Azharsyah Ibrahim

This study was motivated by the polemic on the importance of class attendance in a learning process in the institutions of higher education. This article aims to examine the influence of class attendance on students’ grades using quantitative approach. Primary data was collected from four classes of Department of Islamic Banking Diploma (D-III PS) and Department of Islamic Economics of State Islamic University (UIN) Ar-Raniry, Banda Aceh. Data was then analysed using several stastistical techniques from the SPSS. The result showed that statistically there was a significant positive correlation between class attendance and students’ grades. The findings clearly demonstrated the importance of class attendance in attaining the good grades among university students within the two departments. Nevertheless, analyzing the results more closely indicated that class attendance was not a direct factor in enhancing the students’ grades. It only functioned as a mediator and trigger for the emer...

International e-journal of educational studies

Munir Sahin

Mediterranean Journal of Educational Research

It is an important element that students should attend the class regularly to be successful in all educational institutions practicing formal education. The aim of the research is to stduy the relation between the academic achievement and class attendance rank of 46 preservice teachers attending the 4th grade of the department of science education in primary education in Pamukkale University. This reserach in which the mixed method was used adopted both quantitative adn qualitative research techniques. In this research survey model is adopted from the point of quantitative method and case study model is adopted from the point of qualitative method. The paradigm of the reserach from the point of quantitative method is constituted by science preservice teachers taking astronomy lessons. Intentional sample selection is adopted in the reserach. The data of the reserach gathered adopting the technique semi-structured interview techniques from the point of qualitative method. The semi-structured interviews are recorded by tape recorder, then, the research data is observed as thematic turning the records into detailed written documentary and content is analysed. Either directly or indirectly, both intramaturally and extramaturally,education is a process of assisting individuals edify and acquire knowledge,skill and understanding essential for having a part in community life. School is an educational organisation in which students are made obtain terminal behaviours,knowledge and skill in accordance with fundamentals and general and special purposes of the education system through scientific methods (Demirtas and Gunes,2003,s.109). Attendance of students must be implemented in order that the school can carry out its functions. Active participation principle is carried out thorugh the implemention of students' attendance. Education oriented motivation level of students attending classes increases when active participation is used as a base. Academic achievement is defined as students' achievement level of the intended behaviours in school life(Silah, 2003, s. 103). Academic achievement in higher education is used as a criterion in determining whether the students obtained the terminal behaviours or not. Besides, higher education is a significant criterion in job and academic career applications later on. In brief, ensuring the students' attendance increases the academic achievements and the increase in students' achievements can insure their getting a good job, having an academic career and leading a comfortable life. It is observed in literature that limited study has been done related to attendance and absenteeism. These studies can be summarized like the followings; Kadı(2000) searched the constant absenteeism reasons of secondary school students in Adana. In his research Pehlivan(2006) extracted three different results consisting of reasons arise from students; from students' pedagogical conditions and from students' private conditions. In a research named "the reasons for the absenteeism of students and the reflection of absenteeism in academic achievement" Altınkurt(2008) has reached 6 different results arising from personal reasons, academic _____________

steeven espiritu

Meshal Pervaiz

In this study, the research team studied the effects of student's attendance on academic performance; with the major objective of the study is to investigate the relationship between student attendance and academic performance and to examine factors that affect student attendance at SIMAD University. Sample size of 100 students was selected from SIMAD University students, especially faculty of Business and Accountancy, last semester students. Both primary and secondary data was used in order to answer research questions. Questionnaire and content analysis were used as research instrument. The study found that there is a moderate positive relationship between student attendance and academic performance. Based on the findings, the researchers suggest that all students, particularly prospective students and those students who are not as academically strong, to be informed about the importance influence of class attendance on academic performance. And also the study recommended that universities should maintain or develop strict guidelines for student attendance and monitor factors that could hinder a student from attending class on a regular basis.

European of Social …

Dr. Azizi Yahaya

Procedia - Social and Behavioral Sciences

Quality & Quantity

Pilar Aparicio-Chueca , Maribel Cebollero

Journal of Social Sciences

Jamaludin Ramli

In this study, the research team studied the effects of student’s attendance on academic performance; with the major objective of the study is to investigate the relationship between student attendance and academic performance and to examine factors that affect student attendance at SIMAD University. Sample size of 100 students was selected from SIMAD University students, especially faculty of Business and Accountancy, last semester students. Both primary and secondary data was used in order to answer research questions. Questionnaire and content analysis were used as research instrument. The study found that there is a moderate positive relationship between student attendance and academic performance.

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Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing

Andrew J. K. Phillips 1 , 2 na1 ,
William M. Clerx 1 , 2 na1 ,
Conor S. O’Brien 1 ,
Akane Sano 3 ,
Laura K. Barger 1 , 2 ,
Rosalind W. Picard 3 ,
Steven W. Lockley 1 , 2 ,
Elizabeth B. Klerman 1 , 2 &
Charles A. Czeisler 1 , 2

Scientific Reports volume 7 , Article number: 3216 ( 2017 ) Cite this article

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Computational science
Human behaviour
Neurophysiology

The association of irregular sleep schedules with circadian timing and academic performance has not been systematically examined. We studied 61 undergraduates for 30 days using sleep diaries, and quantified sleep regularity using a novel metric, the sleep regularity index (SRI). In the most and least regular quintiles, circadian phase and light exposure were assessed using salivary dim-light melatonin onset (DLMO) and wrist-worn photometry, respectively. DLMO occurred later (00:08 ± 1:54 vs. 21:32 ± 1:48; p < 0.003); the daily sleep propensity rhythm peaked later (06:33 ± 0:19 vs. 04:45 ± 0:11; p < 0.005); and light rhythms had lower amplitude (102 ± 19 lux vs. 179 ± 29 lux; p < 0.005) in Irregular compared to Regular sleepers. A mathematical model of the circadian pacemaker and its response to light was used to demonstrate that Irregular vs. Regular group differences in circadian timing were likely primarily due to their different patterns of light exposure. A positive correlation (r = 0.37; p < 0.004) between academic performance and SRI was observed. These findings show that irregular sleep and light exposure patterns in college students are associated with delayed circadian rhythms and lower academic performance. Moreover, the modeling results reveal that light-based interventions may be therapeutically effective in improving sleep regularity in this population.

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Introduction

The sleep of college students is often variable in both duration and timing, with many students suffering from considerable sleep deficiency 1 , 2 , 3 , 4 , 5 . In adults, short sleep duration has been associated with cognitive impairments, including increased reaction time and reduced cognitive throughput 6 ; motor vehicle accidents and early mortality 7 ; elevated risk for metabolic disorders, including obesity 8 , type 2 diabetes 9 , and cardiovascular disease 10 ; and psychiatric disorders 11 . Sleep is multidimensional, however, and its importance to health and performance may not be purely dependent upon its daily duration. The composition of sleep varies depending on circadian phase and the time of day at which sleep occurs 12 , 13 . Circadian phase is affected by light exposure; even room light shifts circadian phase significantly in humans 14 . Individuals who frequently change their sleep timing, and consequently their pattern of light/dark exposure, may experience misalignment between the circadian system and the sleep/wake cycle, since the circadian clock takes time to adjust to schedule changes 15 . Such misalignment may have an adverse effect on both cognitive function and health 7 , 16 .

To date, researchers have analyzed variability in measures associated with nighttime sleep episodes, such as total nighttime sleep, midpoint of the nighttime sleep episode, nighttime sleep onset, or morning awakening time 1 , 3 , 17 , 18 , 19 , 20 , including two recent studies that correlated variance in these measures with weight gain 21 and poor academic performance 22 . Variables based on the timing of nighttime sleep episodes may be difficult to generalize to individuals with extremely irregular sleep, polyphasic sleep, or rotating schedules, because these individuals often have no identifiable nighttime sleep episode, many daytime sleep episodes, or nights with no sleep (all-nighters). A measure of inter-daily stability has been proposed for quantifying regularity in activity measures 23 , but this metric quantifies overall variability in a time-signal after averaging across days, rather than quantifying how rapidly sleep patterns change between consecutive days. When considering the biological impact of irregular sleep, rapid changes in sleep timing are important to quantify, because they are particularly challenging for the circadian system to accommodate. Chronic jet-lag induced by constantly shifting schedules increases mortality 24 and tumor growth rate 25 in mice, while rotating night shift work is associated with increased risk of heart disease 26 and breast cancer in humans 27 . One previous study of college students collected data from regular sleepers, defined as individuals who habitually slept from midnight to 08:00 for 7–8 h, and irregular sleepers, defined as individuals whose sleep/wake times varied by “about 2–4 h” 18 . That study found that regular sleepers have better mood and psychomotor performance, and increased time in REM and slow-wave sleep.

Motivated by our interest in capturing changes in sleep timing on a day-to-day (circadian) timescale, we constructed a novel metric for sleep regularity, called the Sleep Regularity Index (SRI). This index calculates the percentage probability of an individual being in the same state (asleep vs. awake) at any two time-points 24 h apart, averaged across the study. The index is scaled so that an individual who sleeps and wakes at exactly the same times each day scores 100, whereas an individual who sleeps and wakes at random scores 0. This index is constructed on the reasoning that changes in sleep schedules from one 24-h interval to the next may cause circadian disruption and thus impact normal biological functioning and health. The SRI differs from previous approaches in that it does not require designation of a main daily sleep episode, and can thus be applied in populations such as college students, where additional daytime sleep episodes and all-nighters are commonly observed.

Using the SRI, we assessed real-world sleep patterns in college undergraduates and classified individuals as Regular (top quintile) or Irregular (bottom quintile). We examined the relationships among SRI, sleep duration, distribution of sleep across the day, and academic performance during one semester. In addition, we measured the phase of the endogenous circadian melatonin rhythm and light exposure patterns in participants classified as Regular or Irregular. Differences in circadian timing of endogenous melatonin secretion and sleep propensity between Regular and Irregular sleepers could potentially be due to systematic differences in circadian physiology. For example, Irregular sleepers could have longer intrinsic circadian periods, leading to delayed circadian rhythms 28 and increased overlap of sleep with morning classes, leading to irregular sleep schedules. Alternatively, the difference in circadian timing could be due to different patterns of light exposure associated with Regular vs. Irregular sleepers, because light exposure during the early biological night delays the circadian clock 29 . We tested these mechanistic hypotheses using a previously-validated mathematical model of the human circadian clock and its response to light.

Sleep regularity is independent of sleep duration

Individual sleep patterns across the 30 days ranged from highly irregular to highly regular (SRI range: 38–86, mean ± std = 73 ± 11). The distribution of SRI was negatively skewed and non-normal (p < 0.02, Kolmogorov-Smirnov). Daily average sleep duration ranged from 5.7–9.3 h (mean ± std = 7.4 ± 0.7 h) and followed a normal distribution (p = 0.96, Kolmogorov-Smirnov). Examples of individual sleep patterns are shown in Fig. 1 . In this population of students living under real-world academic and social constraints, there was no correlation between average daily sleep duration and SRI (r = 0.05, p = 0.71).

Two dimensions of sleep: duration and regularity. ( A ) Average daily sleep duration vs. sleep regularity index (SRI) for all participants ( n = 61) assessed across the whole study interval. Participants identified for the Irregular ( n = 12) and Regular ( n = 12) groups at the study mid-point are red circles and blue squares, respectively. Other individuals are white triangles. As explained in the Methods, the SRI values differed slightly by end of study, so those identified as most extreme at the study midpoint did not necessarily remain most extreme at end of study; however, the differences between Regular and Irregular groups remained highly significant (see Results). Error bars indicate mean and standard deviation for Regular and Irregular groups in both SRI and sleep duration. Sleep patterns for four participants collected using daily diaries are shown using double-plotted raster diagrams, where black bars indicate episodes of sleep and gray bars indicate missing data. Four examples are displayed: ( B ) an Irregular long sleeper, ( C ) an Irregular short sleeper, ( D ) a Regular long sleeper, and ( E ) a Regular short sleeper.

At the study midpoint (using data from days 1–14), we identified the 12 individuals in the lower quintile (SRI range: 35–64, mean ± std = 52 ± 10, the “Irregular” sleepers) and the 12 individuals in the upper quintile (SRI range: 81–87, mean ± std = 84 ± 2, the “Regular” sleepers). We note that any measure of sleep regularity will require more data to reliably estimate for an irregular sleeper than for a regular sleeper, and no measure of sleep regularity can be perfectly estimated from a finite interval of study. As can be seen in Fig. 1 , there are therefore participants who would have qualified for the Regular or Irregular groups based on the full 30 days, but were not selected at study midpoint. Nevertheless, our Irregular and Regular groups remained strongly separated by SRI, indicating a stable difference between these extremes. At the end of the 30 days, the SRI of the Irregular (range: 38–69, mean ± std = 56 ± 10) and Regular (range: 78–87, mean ± std = 83 ± 3) groups remained significantly different (p < 10 −4 , rank-sum test).

There was no significant difference in average daily sleep duration between the Irregular (7.16 ± 0.64 h) and Regular (7.27 ± 0.59 h) groups (p = 0.68, t-test). On baseline questionnaires, Irregular sleepers reported, relative to Regular sleepers, poorer sleep quality on the Pittsburgh Sleep Quality Index (6.83 ± 2.39 vs. 3.75 ± 2.39; p < 0.01), later mid-sleep time on free days (7:05 ± 1:23 vs. 4:53 ± 0:56; p < 0.001), and later average diurnal preference (more ‘evening-type’) on the Morningness-Eveningness Questionnaire (40.1 ± 6.2 vs. 54.3 ± 10.2; p < 0.001). We did not find any significant difference in the sex distribution of our groups: the Regular group had 6 M 6 F, while the Irregular group had 7 M 5 F.

Irregular sleepers have delayed sleep timing and more daytime sleep

As a group, Regular sleepers expressed a robust daily rhythm in the percentage of time they spent asleep averaged across the day in 1-h time bins (Fig. 2A ). As summarized in Table 1 , Regular sleepers obtained significantly more sleep during the clock night (defined as 22:00 to 10:00) and significantly less sleep during the clock day (defined as 10:00 to 22:00) than Irregular sleepers. Regular sleepers were asleep 55% of the clock night and only 1% of the clock day. By contrast, Irregular sleepers were asleep for 42% of the clock night and 11% of the clock day.

Sleep/wake and light/dark cycles differ between Regular and Irregular groups. Gray lines show individual data ( n = 12 for each sleep/wake panel and n = 11 for each light/dark panel). Colored lines show group mean and standard deviation in one-hour bins, with data for each individual averaged across the whole study interval (i.e., multiple days). Dark gray bars indicate clock night (22:00 to 10:00). Left panels: Sleep/wake rhythm (percentage of time asleep) for ( A ) Regular and ( B ) Irregular sleepers. Right panels: Normalized light levels for ( C ) Regular and ( D ) Irregular sleepers.

As expected, Irregular sleepers (Fig. 2B ) averaged more daytime sleep episodes (naps) per week than Regular sleepers (3.02 ± 1.47 vs. 0.75 ± 0.80; p < 0.002, rank-sum test) and obtained more daytime compensatory sleep per week (5.35 ± 2.82 h vs. 0.72 ± 0.65 h; p < 0.0005, rank-sum test). The fitted peak of the sleep propensity rhythm (i.e., the daily rhythm in percentage likelihood of being asleep) was significantly later for the Irregular group; 95% confidence intervals for the time of the peak for the first harmonic of a two-harmonic fit were 06:33 ± 0:19 in the Irregular sleepers vs. 04:45 ± 0:11 in the Regular sleepers (p < 0.005).

Sleep onset and morning awakening times differed significantly between groups. In Irregular sleepers, the average subjectively-reported time of sleep onset was 03:02 ± 1:23 vs. 01:15 ± 0:51 in Regular sleepers (p < 0.001, rank-sum test). In Irregular sleepers, the average time of morning awakening was 10:00 ± 1:41 vs. 08:27 ± 0:51 in Regular sleepers (p < 0.03, rank-sum test).

Interestingly, the Irregular and Regular groups, which were defined using SRI, did not always significantly differ by other commonly-used metrics of sleep variability. Standard deviations of sleep onset times (2.05 ± 0.72 h vs. 1.20 ± 0.16 h; p < 0.001, rank-sum test) and wake times (2.08 ± 0.99 h vs. 1.10 ± 0.48 h; p < 0.01, rank-sum test) were significantly different between Irregular and Regular groups. However, the standard deviation of mid-sleep time (1.64 ± 0.64 h vs. 0.99 ± 0.26 h; p = 0.053, rank-sum test) did not significantly differ between Irregular and Regular groups.

Irregular sleepers have a lower amplitude light rhythm

Irregular sleepers received different patterns of light exposure (Fig. 2C and D ), with summary metrics in Table 1 . The amplitude of the daily light/dark cycle (light rhythm) was lower in Irregular sleepers, reflecting a smaller difference between day-time and night-time illuminance. 95% confidence intervals for the first-harmonic amplitude of a two-harmonic fit were 102 ± 19 lux (Irregular) vs. 179 ± 29 lux (Regular; p < 0.005). Irregular sleepers received significantly less day-time light (Table 1 ) and had a broader range of light-exposure centroid times (9.6 h vs. 4.5 h). On average, light centroid times were later in the Irregular sleepers than in Regular sleepers, although this difference was not significant (14:18 ± 2:37 vs. 13:05 ± 1:19; p = 0.18, rank-sum test). When light levels were normalized on an individual basis, by dividing by that individual’s average daily illuminance, Irregular sleepers were found to receive relatively more light during the biological night (DLMO to 10 h post-DLMO) and clock night (Table 1 ). Although the light pattern appeared slightly delayed in the Irregular group, phase parameters for the two-harmonic fits were not significantly different between groups. We note that parametric fits are not ideal for quantifying the effects of the light pattern on the circadian pacemaker, since the sensitivity of the circadian pacemaker to light varies across the day and with previous light exposure history. This point is addressed below by our use of a mathematical model to explicitly predict an individual’s circadian phase of entrainment from the individual light patterns.

Irregular sleepers have delayed onset of melatonin secretion, which is predicted by their patterns of light exposure

On average, Irregular sleepers had significantly later DLMO (00:08 ± 1:54 vs. 21:32 ± 1:48, p < 0.003) (Fig. 3A–C ). This group difference remained significant even when the earliest individual in the Regular group was removed (p < 0.005). When light exposure patterns were given as inputs to a mathematical model of the human circadian pacemaker, with all other model parameters fixed at default values (i.e., assuming no inter-group differences in circadian physiology), the model predicted an average 1.7 h delay (p < 0.01, t-test) in DLMO timing in the Irregular group compared to the Regular group, whereas the actual average delay of the Irregular group compared to the Regular group was 2.2 h. An example of model inputs and outputs is shown in Fig. 4 . On an individual basis, predictions were less accurate (11 Regular and 10 Irregular individuals had viable light data for modeling). In the Regular group, 4 of 11 predictions were within ± 1 h of the observed DLMO timing, and 9 of 11 were within ± 2 h. In the Irregular group, 5 of 10 predictions were within ± 1 h, and 8 of 10 were within ± 2 h. Linear fits to Regular (r = 0.50, slope = 0.31) and Irregular (r = 0.34, slope = 0.26) groups both had slopes less than 1, implying the model predicted less inter-individual variability in DLMO timing within each group than existed in the data. Within the participants in whom we assessed DLMO, we found that SRI and DLMO were negatively correlated (r = −0.66, p < 0.001, Fig. 5B ).

Melatonin secretion is delayed in Irregular sleepers. Top panel: ( A ) Timing of salivary dim light melatonin onset (DLMO) for Regular ( n = 12) and Irregular ( n = 12) groups. Individuals in each group are shown as dots, with y-axis position jittered for visibility. Groups means (triangles) and standard deviations (error bars) are shown. Middle two panels: Time course for salivary melatonin concentration are shown for Regular participants in ( B ) and Irregular participants in ( C ), along with average sleep midpoint times for the nighttime sleep block for each individual (black dots), with y-axis position jittered for visibility. Gray lines show individuals in 1-h bins. Colored lines with error bars show group mean and standard deviation in 1-h bins. Bottom panel: ( D ) Actual timing of DLMO vs. model prediction for timing of DLMO, assessed using saliva. Data points correspond to individuals in the Regular (blue square) and Irregular (red circle) groups. Error bars show mean and standard deviation for each group. Differences in group averages are displayed. The dashed lines show linear regressions for each group.

Example of model inputs and outputs for one participant. Variables are shown for days 280–300 of a 300-day simulation for one participant from the Regular group. ( A ) Binned light levels in lux. ( B ) The two circadian pacemaker variables, x (blue) and x c (red). ( C ) The predicted salivary melatonin concentration. ( D ) The clock-time of Dim Light Salivary Melatonin Onset (DLSMO) on each day.

Correlations between sleep regularity index (SRI), grade point average (GPA), and timing of melatonin secretion. Panels (A, B and C) show the relationships between the variables: SRI, GPA, and salivary DLMO. Dashed lines show the linear fits, with r-values and p-values shown for each linear (Pearson) correlation. Each data point represents an individual, with colors indicating whether the individual was a member of the Regular (blue), Irregular (red), or neither group (black). Note that DLMO was only assessed in the Irregular and Regular participants.

Sleep regularity is positively associated with academic performance

SRI had a positive linear correlation with Grade Point Average (GPA) in the whole sample ( n = 59, Pearson r = 0.37, p < 0.004; Fig. 5A ). An increase of 10 in SRI was associated with an average increase of 0.10 in GPA. For reference, median GPA at Harvard has been recently estimated 30 as 3.64, with a maximum possible 4.00. As secondary analysis, we calculated GPAs for the initially designated Irregular (3.41 ± 0.33) and Regular (3.60 ± 0.32) groups; the difference in these subgroups ( n = 12 each) was not significant (p = 0.16). When Irregular and Regular groups were designated using the full 30-day sleep record, rather than 14 days, there was a significant difference in GPA (p < 0.02) between Irregular (3.42 ± 0.34) and Regular (3.72 ± 0.24) groups. There was no significant linear correlation between sleep duration and SRI (r = 0.13, p = 0.29) or sleep duration and GPA (r = 0.12, p = 0.37). DLMO was also not significantly correlated with GPA (r = 0.37, p = 0.08). Since sleep timing was found to be associated with SRI, we tested whether the pre-study score on the Morningness-Eveningness Questionnaire was predictive of GPA, but found no significant correlation (r = −0.01, p = 0.96).

To determine whether our results were dependent upon our choice of regularity metric, we tested relationships between SRI, GPA, and previously used metrics for sleep regularity that are based only on nighttime sleep. SRI had highly significant negative linear (Pearson) correlations with standard deviations of sleep onset time (r = −0.66, p < 10 −8 ), wake time (r = −0.62, p < 10 −7 ), and mid-sleep time (r = −0.62, p < 10 −7 ). We also found negative linear correlations between GPA and standard deviations of sleep onset time (r = −0.43, p < 0.001), wake time (r = −0.29, p = 0.02), and mid-sleep time (r = −0.31, p < 0.02). These results collectively demonstrate a robust positive relationship between sleep regularity and academic grades. We emphasize, however, that this is an association, and we cannot determine causality.

Our findings demonstrate that irregular sleep schedules in a specific population of college students are associated with a significant circadian phase delay in the timing of both the endogenous melatonin rhythm and in the sleep propensity rhythm—equivalent to traveling two to three time zones westward—compared to students on a more regular sleep/wake schedule. We also found that sleep regularity is positively correlated with academic performance. Sleep regularity was uncorrelated with sleep duration, suggesting that regularity captures another informative dimension of sleep. The SRI metric we used here captures a specific type of regularity–day-to-day differences in sleep patterns–and does not require a main daily sleep episode to be designated, which is advantageous in populations with highly irregular sleep patterns. We note that this metric may be complemetary to another metric recently devised to capture day-to-day changes in timing of the main daily sleep episode 31 .

Our findings are consistent with a previous study 32 that found later wake times are associated with worse grades in first-year college students. Our results suggest this association may be mediated by sleep regularity. This interpretation is consistent with an earlier study that identified irregular sleep as a risk factor for worse academic performance in medical students 33 . We anticipated that Irregular sleepers might face decreased sleep opportunities due to conflicts between their delayed, erratic schedules and classes. Instead, we found that Irregular sleepers had the same total sleep as Regular sleepers. They achieved this by sleeping more during the daytime. This suggests that homeostatic control of sleep functions similarly in both groups, forcing Irregular sleepers to have compensatory daytime sleep episodes when they obtain insufficient nighttime sleep, although we do not have measures of sleep intensity by which we could quantify the dynamics of sleep homeostasis. This pattern of sleep is similar to blind individuals with non-24-hour sleep/wake disorder–they maintain the same total sleep duration via compensatory daytime sleep episodes, even when their sleep is highly fragmented due to their sleep/wake cycle being out-of-sync with their circadian cycle 34 .

Differences in academic performance were not associated with average sleep duration in our population, and our data suggest that polyphasic sleep schedules that distribute sleep around the clock may be less effective for students, even if they maintain total sleep time. We note, however, that we cannot establish a causal relationship between sleep patterns and academic performance; sleep regularity may indeed be a proxy for regularity in other aspects of daily activity and schedules. The ability to sleep during the daytime as a compensation strategy after insufficient nocturnal sleep may also be specific to undergraduate students living on campus. This strategy would not be available to most adults who work full time or to students who do not live near campus. In populations that are limited in their ability to nap, sleep duration may positively correlate with SRI.

The association between SRI and circadian timing has at least two competing mechanistic hypotheses. One hypothesis is that Regular and Irregular sleepers differ in their circadian physiology. For example, Irregular sleepers could have longer intrinsic circadian periods 35 . Under this hypothesis, irregular sleep schedules would be a consequence of delayed circadian rhythms, which would promote later sleep onset and conflict with early class schedules, leading to irregular sleep patterns.

A countervailing hypothesis is that Regular and Irregular sleepers do not differ in their circadian physiology. Under this (null) hypothesis, differences in circadian timing would be a consequence of irregular sleep schedules and their associated light patterns. Using a mathematical model of human circadian rhythms, we conclude that the results are primarily consistent with the latter (null) hypothesis. While there may be differences in circadian physiology between the groups, which may also account for individual variability that the model fails to capture, these are not the primary reason for the group differences. We therefore conclude that Irregular sleepers have later circadian timing predominantly due to the characteristics of their light profiles: these can be summarized as lower-amplitude daytime light exposure, together with a relatively greater ratio of nighttime light exposure to daytime light exposure. Increased exposure to daytime light desensitizes the circadian clock to the effects of light at nighttime 36 , which may help protect Regular sleepers from the delaying effects of exposure to electronic light-emitting devices in the early biological night 37 , 38 . Moreover, insufficient exposure to light in the early biological day would be predicted to reduce the amount of corrective phase advance in Irregular sleepers 39 .

A potential limitation in our ability to predict DLMO timing at the individual level is the fact that melatonin was assessed on only one day. It is not well understood how stable DLMO timings are in a college student population, but one would expect variable light patterns to cause some shifting from day to day, as is predicted by our model. Individuals with irregular sleep or light patterns in particular may have less stable melatonin phases, which could account for a data vs. model mismatch. This is consistent with our observation that the correlation between data and model was weaker in the Irregular group than in the Regular group.

Light studies in humans and other animals have demonstrated that the intrinsic period of the circadian pacemaker is dependent on prior light exposure 40 , 41 . A light pulse that delays the circadian rhythm also usually causes a transient lengthening of the circadian period, which may last for weeks 42 . This plasticity is potentially germane to our results, since a longer circadian period theoretically implies a later phase angle of entrainment, given the same light pattern and the same sensitivity of the circadian system to light 43 . Recently, it was reported that individuals with delayed sleep phase disorder have unusually long intrinsic circadian periods, as measured under an ultradian forced desynchrony protocol 44 . It is therefore plausible that late or irregular schedules could induce long-lasting changes to the circadian clock, including lengthening of the intrinsic circadian period, which would further encourage late or irregular schedules.

Why certain individuals develop irregular sleep is an important question not directly addressed by our study. Individuals who are biologically predisposed to later schedules may find this amplified by use of inappropriately timed light. This hypothesis is supported by a recent study that found large inter-individual differences in circadian timing vanished when individuals were exposed to only natural outdoor light 45 . Other studies have linked eveningness with lower self-control 46 , behavioral/emotional problems in adolescents 47 , and depressive symptoms 48 , 49 . These factors may interact. For example, lower self-control may reduce efforts to maintain a stable bed-time or reduce use of electric light at night, while depression may decrease motivation to maintain a regular morning schedule or obtain regular physical activity, decreasing daytime light. Sex differences may also contribute to differences in sleep timing 35 , 50 , but our experiment was not powered to test for interactions between sex, sleep timing, and regularity. We also did not attempt to control for phase in the menstrual cycle.

Our findings could potentially be used to design and test interventions. Delayed circadian rhythms and irregular sleep patterns are associated with weight gain 21 and poor academic grades 22 . Although further experiments are needed to identify factors that predispose individuals to adopting irregular sleep, our results suggest this could be treated in undergraduates through light interventions used to advance circadian rhythms, and education about importance of regular sleep schedules. Adoption of a stable sleep pattern would regulate light/dark cycles, reinforcing regular behavior. Recent empirical findings suggest that effective light interventions could easily be developed at low user burden 51 . Notably, one study that experimentally enforced regular sleep schedules for 38 days in college students with habitually irregular sleep patterns found no change in time spent in each sleep stage, auditory vigilance, addition test performance, or mood between conditions 52 . However, that 1982 study was underpowered ( n = 12) and confounded by many factors, including pooling of data from an unsuccessful pharmaceutical trial, such that benzodiazepines were administered each night to half ( n = 6) of the participants; inconsistent timing of both sleep and performance testing between participants; and self-administration of performance tests under uncontrolled conditions. Indeed, a more recent study in college undergraduates found that experimentally enforcing regular schedules for 28 days, with a minimum of 7.5 h daily sleep, improved subjective alertness compared to schedules with the same minimum sleep duration but no requirements on sleep regularity 17 . In light of the findings from our study, the question of whether imposition of a regular schedule can improve sleep, health, and performance should be revisited in an experimental design.

In summary, our results demonstrate that irregular sleep is associated with delayed circadian timing, and that most of this delayed timing can be explained, using a mathematical model, by the differences in patterns of light exposure. This is important, because it suggests a feedback loop between an individual’s sleep regularity, their sleep timing, and their light exposure pattern. Individuals who adopt irregular sleep patterns are subject to a light pattern that encourages circadian delay and thus may lead to reinforcement of delayed and irregular sleep patterns. This suggests that light-based interventions may be successful in treating irregular sleep in this population. While the data here cannot be used to make causal inferences regarding the relationships between sleep regularity, circadian timing, and academic performance, our findings nevertheless highlight sleep regularity as a potentially important and modifiable factor – independent from sleep duration – in determining academic performance and circadian timing.

Participants

Full-time undergraduates (excluding first-years), aged 18+, were recruited from Harvard College. Enrollment was not based on class schedule or types of classes. Participants were excluded if pregnant or traveling >1 time-zone one week before or during study. 63 participants were enrolled. Two discontinued in the first week for personal reasons. Remaining participants (32 M 29 F) were aged 18–24 (20.23 ± 1.27). One participant selected for the Irregular group discontinued for personal reasons. The next eligible participant was invited as replacement and successfully completed the study.

Study approval

All participants provided written informed consent. Research was approved by the Partners Health Care Human Research Committee and the Committee on the Use of Human Subjects at Harvard University, and was in compliance with HIPAA regulations and the Declaration of Helsinki. The study did not meet the criteria for a ClinicalTrials.gov registration.

Participants lived in campus housing and reported their self-selected sleep/wake schedule for ~30 days (26–36 days, mean 30.67 days) by online diary during Fall 2013. After consent, participants completed the Pittsburgh Sleep Quality Index 53 and Horne-Östberg Morningness-Eveningness Questionnaire 54 . Individuals self-reported their current GPA after study. On day 15, we classified participants using SRI. The highest ( n = 12) and lowest ( n = 12) quintiles were selected to wear actiwatches and complete dim-light saliva collection. Participants were blinded to group (i.e., they did not know that they had been assigned to the most regular or most irregular group, only that they had been selected for further study). Diaries were based on ones previously validated against the gold-standard for sleep assessment (polysomnography) in resident physicians 55 and used in shift-workers 56 . Participants completed diaries shortly after awakening to report time of sleep onset, morning awakening, and the timing and duration of any awakenings during their main daily sleep episode. Participants also reported the timing and duration of any other sleep episodes (naps) and any actiwatch removals. To ensure accuracy of entries, each diary was accessible for only 24 h to prevent post-hoc completion; reminders were sent at 08:00. In addition, online diaries were checked daily by study staff, and participants with errors or missing fields were contacted within 24 h to encourage completion and clarify any errors. Using this highly-supervised approach, sleep/wake state was provided for 95% of the study across all individuals; individual completion rates ranged from 72–100%. For day 15 classification, data from days 1–14 were used. Only pairs of non-missing time-points were used to compute SRI. For final analysis, SRI was computed using the longest interval of a whole number of weeks with no missing data to avoid day-of-week bias (i.e., a time interval that is a multiple of 7 days). 2 individuals had one week, 7 had two weeks, 7 had three weeks, and 43 had four weeks. We note that the interval of sleep diary collection overlapped a daylight savings time transition (November). Differences in SRI calculated with a universal time or a daylight-savings-corrected time were negligible, so this did not affect group selection for the later light and melatonin collection.

Light data were obtained minute-by-minute from 22 of 24 participants (11 Regular and 11 Irregular) using Motionlogger-L (Ambulatory Monitoring, Inc., Ardsley, NY) for approximately one week after the study midpoint. For one participant, the device malfunctioned. Another failed to return the watch. All actiwatches were tested against a calibrated light meter for 5–10 min at ~1–5 lux and levels ranging ~90–3000 lux. At 1–5 lux, all actiwatches were within 5 lux of the light meter. From 90–3000 lux, all watches were within 0.25 log 10 -units of the light meter. Saliva samples were collected hourly from 7 h prior to 3 h after habitual bedtime on a Friday night at the end of the study. There were no restrictions on prior sleep or class attendance. Participants were permitted to take brief naps between samples, interact with others, and use non-light emitting entertainment (e.g., books, board games, music). Participants began ≤5 lux conditions at least 45 minutes before first sample. Participants were instructed to avoid consuming foods and beverages that might impact melatonin levels, i.e., citrus fruits, bananas, and milk. They were also instructed to avoid eating or drinking, and to maintain stable posture, for the 20 min prior to each sample. 13 participants completed saliva collection in a central on-campus room supervised by investigators. The other 11 completed the protocol in individual on-campus rooms. An investigator visited rooms to ensure appropriate dim-light conditions, and light levels were confirmed using the actiwatches. All participants were additionally provided filtered goggles, in case of unanticipated light exposure, and red nightlights for rooms where light was necessary (e.g., bathrooms) to minimize melatonin suppression 57 . Samples were frozen upon collection. This procedure for assessing circadian phase in the field is based on a validated protocol with a field success rate of 62.5% compared to in-lab DLMO 58 . Salivary melatonin concentration was determined by standard RIA with analytical sensitivity of 0.2 pg/mL and an intra-assay %CV of 10.8% at 2.1 pg/mL and 11.4% at 20.4 pg/mL (BÜHLMANN Direct Saliva Melatonin RIA, Schönenbuch, Switzerland). DLMO time was determined by interpolating points immediately above and below a threshold of 5 pg/mL. In one participant, the first assay of 5.76 pg/mL was slightly above threshold, with values then rising. To compute DLMO we could extrapolate back to the estimated time of 5.00 pg/mL (19:25) or take the first assay time as DLMO (19:42). Since the individual was in the Regular (earlier) group, we used the latter assumption to be conservative.

Statistical comparisons between groups were computed using the non-parametric Wilcoxon rank-sum test, unless there was strong evidence of a normal distribution, in which case a two-tailed t-test was used. The average number of naps per week and amount of time spent napping was computed using the longest interval of a whole number of weeks with no missing data. For participants in Regular and Irregular groups, average clock time of sleep onset, midsleep, and wake time were calculated using vector averages. These timings were also computed by traditional means (linear averaging) for comparisons with SRI. The centroid of light exposure time was calculated using a weighted vector average, after averaging illuminances in 1-h bins with respect to clock time, and weighting each bin in proportion to its average illuminance. Grade Point Average (GPA) was reported by 59 participants. The two non-reporters were neither Regular nor Irregular. As primary analysis, we computed the correlation between SRI and GPA. As secondary analysis, we tested for significant differences in GPA between Regular and Irregular groups.

Sleep and light data were averaged in 1-h time bins, using a weighted average for each individual with equal representation for each of day of the week. To determine amplitude and phase of sleep and light rhythms, two-harmonic fits were performed using the least-squares estimator nlinfit in Matlab. Confidence intervals for model parameters were computed using nlparci. Night was defined in two ways: clock time (22:00 to 10:00) and biological time (DLMO to 10 h post-DLMO, based on typical timing of melatonin release 59 ). Only data from before dim-light salivary melatonin assessment were analyzed. Intervals of usable light data ranged 89–335 h (mean of 171 h). SRI was computed as the likelihood that any two time-points (minute-by-minute) 24 h apart were the same sleep/wake state, across all days. The value could theoretically range 0 to 1, and was rescaled ( y = 200 ( x − 1/2)) to give a range of −100 to 100. This rescaling was chosen to give a more intuitive range. In practice, individuals will only display sleep patterns that range between an SRI of 0 (random) and 100 (periodic). Values less than 0 are still theoretically possible (e.g., sleep for 24 h, wake for 24 h, etc.), but very unlikely to be observed.

A previously validated model of the human circadian pacemaker, its sensitivity to light, and salivary melatonin concentration 60 was used to predict circadian phase. This model has three components: (i) A model of how light is processed by the retina and conveyed as a signal to the central circadian pacemaker. (ii) A limit-cycle oscillator that describes the dynamics of the pacemaker and the phase/amplitude-modifying effects of light. (iii) A multi-compartment model of melatonin release from the pineal gland, diffusion into and elimination from plasma, and diffusion into saliva. Melatonin release from the pineal gland begins and ends at certain circadian phases, with the timing of DLMO being sensitive to the timing at which this begins. In prior work, the time of release was a free parameter, specified in terms of clock time. Here, release time was fixed in terms of oscillator phase, per the approach taken in 61 , to a value of 5.0 radians. This value was selected so that the model’s average predicted DLMO time across all participants aligned with the data.

Light data were input to the model in 1-h non-overlapping bins, using the maximum value in each bin. The light sequence was input repeatedly for 300 days to allow entrainment. For 21 of the 22 individuals this resulted in stable entrainment. For one Irregular individual, entrainment never occurred, even allowing 300 days to entrain, so no model-predicted DLMO could be obtained. Data were used from when individuals received the actiwatch to the day of saliva collection, with data truncated in cases where the individual was clearly not wearing the sensor. Average predicted DLMO time (first crossing of a 5 pg/mL threshold) was used as model DLMO. This approach was taken rather than predicting DLMO at the time of salivary collection, because not all individuals had usable light data up to that day.

This model generates outputs of the circadian clock and estimates of DLMO timing, allowing us to investigate the reasons for group differences in our primary outcome: DLMO timing. We note that this model does not generate sleep/wake patterns, as another recent model does 62 , 63 , as it was not designed for that purpose. We therefore did not include model predictions of sleep/wake patterns.

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Acknowledgements

We thank Omer Zaidi and Michael Shreeve for study coordination and Salim Qadri for diary programming. This research was supported by awards NIH R01-GM-105018, NIH R01-HL-114088, NIH R01-HL094654, NIH P01-AG09975, NIH K24-HL105664, NIH K99-HL119618, NIH R00-HL119618, NSBRI HFP0280, NSBRI HFP02802, and NSBRI HFP02801.

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Andrew J.K. Phillips and William M. Clerx contributed equally to this work.

Authors and Affiliations

Sleep Health Institute and Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA

Andrew J. K. Phillips, William M. Clerx, Conor S. O’Brien, Laura K. Barger, Steven W. Lockley, Elizabeth B. Klerman & Charles A. Czeisler

Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA

Andrew J. K. Phillips, William M. Clerx, Laura K. Barger, Steven W. Lockley, Elizabeth B. Klerman & Charles A. Czeisler

Affective Computing Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

Akane Sano & Rosalind W. Picard

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Contributions

A.J.K.P., W.M.C., C.A.C., E.B.K., S.W.L., and L.K.B. designed the research study. W.M.C. and C.S.O.B. conducted the experiments and acquired the data. All authors analyzed data and wrote the manuscript.

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Correspondence to Andrew J. K. Phillips .

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L.K.B. has previously received research support from Cephalon, NFL charities, Sysco and San Francisco Bar Pilots. She has received consulting/lecture fees or served as a board member for Alertness Solution, Ceridian, Davis Joint Unified School Board, San Jose State University Foundation, Pugot Sound Pilots, Sygma and Torvec. RWP is a co-founder of and shareholder in Empatica Inc and Affectiva Inc and serves on the board of Empatica. She is inventor or co-inventor on over two dozen patents, mostly in the field of affective computing and physiological measurement. She has received royalty payments from MIT for patents licensed to Affectiva, consulting and honorarium payments from Merck, Samsung, Analog Devices, and fees for serving as an expert witness in cases involving wearable sensors from Apple and Intel. Her research is funded in part by a consortium that includes over 70 companies who fund the MIT Media Lab (up to date list is kept online at http://media.mit.edu ) and includes project funding supporting her team’s work from Robert Wood Johnson Foundation, The Simons Foundation, The SDSC Global Foundation, NEC, LKK, Cisco, Deloitte, Steelcase, and Medimmune. She has received travel reimbursement from Apple, Future of Storytelling, Mattel/Fisher-Price, Microsoft, MindCare, Motorola, Planetree, Profectum, Sentiment Symposium, Seoul Digital, Silicon Valley Entrepreneurs Network, and Wired. AJKP holds a patent related to estimating physiological states from measurements of sleep and circadian rhythms. SWL has received consulting fees from Perceptive Advisors, Carbon Limiting Technologies Ltd on behalf of PhotoStar LED, Serrado Capital, Atlanta Hawks; and has current consulting contracts with Headwaters Inc., Wyle Integrated Science and Engineering, PlanLED, Delos Living LLC, Environmental Light Sciences LLC, Hintsa Performance AG, Pegasus Capital Advisors LP, Akili Interactive, Focal Point LLC, OpTerra Energy Services Inc., and Light Cognitive. SWL has received unrestricted equipment gifts from Bionetics Corporation and Biological Illuminations LLC; has equity or stock options in iSLEEP, Pty, Melbourne, Australia and Akili Interactive; advance author payment and/or royalties from Oxford University Press; honoraria plus travel, accommodation or meals for invited seminars, conference presentations or teaching from Estee Lauder, Lightfair, and Informa Exhibitions (USGBC); travel, accommodation and/or meals only (no honoraria) for invited seminars, conference presentations or teaching from FASEB, Hintsa Performance AG, Lightfair, and USGBC. SWL has completed investigator-initiated research grants from Biological Illumination LLC and Vanda Pharmaceuticals Inc and has an ongoing investigator initiated grant from F. Lux Software LLC; completed service agreements from Rio Tinto Iron Ore and Vanda Pharmaceuticals Inc.; and completed three sponsor-initiated clinical research contracts from Vanda Pharmaceuticals Inc. SWL holds a process patent for the use of short-wavelength light for resetting the human circadian pacemaker and improving alertness and performance which is assigned to the Brigham and Women’s Hospital per Hospital policy (2005). SWL has also served as a paid expert on behalf of several public bodies on arbitrations related to sleep, light, circadian rhythms and/or work hours for City of Brantford, Canada, and legal proceedings related to light, sleep and health (confidential). SWL is also a Program Manager for the CRC for Alertness, Safety and Productivity, Australia. EBK received travel reimbursement from the Sleep Technology Council and has served as an expert witness in cases involving transportation safety and sleep deprivation. CAC has received consulting fees from or served as a paid member of scientific advisory boards for: Amazon.com, Inc.; A2Z Development Center; Bose Corporation; Boston Celtics; Boston Red Sox; Cephalon, Inc.; Citgo Inc.; Cleveland Browns; Columbia River Bar Pilots; Gerson Lehman Group; Institute of Digital Media and Child Development; Jazz Pharmaceuticals; Koninklijke Philips Electronics, N.V.; Merck & Co. Inc.; Minnesota Timberwolves; Novartis; Portland Trail Blazers; Purdue Pharma; Quest Diagnostics, Inc.; Samsung Electronics; Sleep Multimedia, Inc.; Teva Pharmaceuticals; Valero Inc.; Vanda Pharmaceuticals; and Zeo Inc.. CAC has also received education/research support from Cephalon Inc., Jazz Pharmaceuticals, Mary Ann & Stanley Snider via Combined Jewish Philanthropies, National Football League Charities, Optum, Philips Respironics, ResMed Foundation, San Francisco Bar Pilots, Schneider Inc., Simmons, Sysco and Vanda Pharmaceuticals, Inc. The Sleep and Health Education Program of the Harvard Medical School Division of Sleep Medicine (which CAC directs) has received Educational Grant funding from Cephalon, Inc., Jazz Pharmaceuticals, Takeda Pharmaceuticals, Teva Pharmaceuticals Industries Ltd., Sanofi-Aventis, Inc., Sepracor, Inc. and Wake Up Narcolepsy. CAC is the incumbent of an endowed professorship provided to Harvard University by Cephalon, Inc. and holds a number of process patents in the field of sleep/circadian rhythms (e.g., photic resetting of the human circadian pacemaker). Since 1985, CAC has also served as an expert on various legal and technical cases related to sleep and/or circadian rhythms including those involving the following commercial entities: Bombardier, Inc.; Continental Airlines; FedEx; Greyhound; Purdue Pharma, L.P.; and United Parcel Service (UPS). CAC owns or owned an equity interest in Apple, Lifetrac, Inc., Microsoft, Somnus Therapeutics, Inc., Vanda Pharmaceuticals, and Zeo Inc. He received royalties from CNN, McGraw Hill, Houghton Mifflin Harcourt, and Philips Respironics, Inc. for the Actiwatch-2 and Actiwatch-Spectrum devices. CAC’s interests were reviewed and managed by Brigham and Women’s Hospital and Partners HealthCare in accordance with their conflict of interest policies.

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Phillips, A.J.K., Clerx, W.M., O’Brien, C.S. et al. Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Sci Rep 7 , 3216 (2017). https://doi.org/10.1038/s41598-017-03171-4

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DOI : https://doi.org/10.1038/s41598-017-03171-4

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A Qualitative Exploration on the Challenges of Transfer Students in an Asian Educational Context

Shirley siu yin ching.

1 School of Nursing, The Hong Kong Polytechnic University, Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, China; [email protected] (S.S.Y.C.); moc.361@iewiewgnahznaillil (L.W.Z.); moc.liamg@dtnymerej (J.N.)

Kin Yuen Tam

2 Department of Applied Social Sciences, The Hong Kong Polytechnic University, Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, China; [email protected]

Lillian Weiwei Zhang

Associated data.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical.

Limited research has been conducted on community college (CC) transfer students’ (TS) experiences in four-year universities, particularly in Asian contexts. To fill this research gap, in this qualitative study, 124 TS from various disciplines in a Hong Kong university participated in 39 focus groups and seven individual interviews. Unlike their Western counterparts, our TS were relatively better prepared and more academically adaptive. Nevertheless, their social integration was restricted by a lack of time for extra-curricular activities, a sense of inferiority and incompetence, and restricted social circles that did not enable interaction with non-TS. These challenges and their implications are discussed. In particular, this study has highlighted differences between the special education systems for CC transfer in Hong Kong and those in Western CC models. The study has also highlighted the study-induced stress, and poor self-perceptions that TS experience, despite their academic abilities.

1. Introduction

Following the massification of higher education, community college (CC) transfer has emerged as a common route of entry to baccalaureate studies, as an alternative to direct post-secondary admission [ 1 , 2 ]. Due to the growing aspirations of CC graduates to pursue bachelor’s degrees in university (also known as “four-year institutions” in the literature), the size of the CC transfer student (TS) population has been booming over the past decade [ 1 , 3 ]. This calls for more attention to CC TS through identifying their characteristics and needs [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. Although there is no shortage of quantitative studies on this subpopulation of university students, particularly in the West, few qualitative studies have been conducted worldwide to understand their transfer experiences.

The highly complex transition processes brought about by transfer can bring about a “turbulent pathway” (p. 600) [ 11 ] to TS, challenging their psychological, academic, and environmental adjustment [ 4 , 5 , 7 , 11 , 12 ]. Driving a host of studies on CC TS [ 13 ], Astin’s theory of student involvement [ 14 ] and Tinto’s model of student integration [ 15 ] posit that both academic and social involvement and integration into the university contribute to students’ persistence in learning and their degree attainment. Transfer student capital, as a synthesis of various concepts such as social capital and cultural capital [ 16 ], refers to the factors bringing about successful transition to university, including but not limited to the processes by which knowledge and experience are acquired by them during baccalaureate studies [ 17 ]. In the existing TS literature, it has remained understudied in how TS capital is manifest and influential on the TS’ learning experience in universities.

A commonly known phenomenon confronting TS is “transfer shock”, defined as the drop in post-transfer GPA compared to previous academic performances in CC [ 10 , 18 ]. Transfer shock is usually attributed to a lack of academic preparation in their prior studies [ 19 ] and personal factors such as family and work commitments [ 20 ]. TS were, therefore, found to be more susceptible to attrition [ 21 ]. They might have heavier study loads than non-TS [ 22 ] and need to demonstrate active and independent learning approaches [ 13 ]. As for social involvement, TS were found to have insufficient interactions with their peers [ 22 ] and to engage less than non-TS in social groups and activities [ 1 ]. Exclusive to TS is a phenomenon coined as “campus culture shock” that can occur when faced with the less personal atmosphere and support systems in university as opposed to those in CC [ 23 , 24 ].

Prior studies have suggested that university management and administrators lack a sound understanding of CC TS’ experiences, leading to “institutional neglect” of these students as a separate population and thereby “overlooking” (p.174) [ 25 ] their needs and the resources available to them [ 25 , 26 ]. Aside from the pool of aforementioned quantitative studies, Tobolowsky and Cox [ 26 ] suggested that a qualitative inquiry into TS’ experiences and perceptions would allow not only a probing of their thoughts and feelings but also some unique examples and anecdotes. In fact, identifying the challenges encountered by TS as an integral part of the university can lead to improved understanding of student mobility and persistence [ 27 ]. Flaga [ 28 ] interviewed 35 TS from various majors, yielding that they perceived the academic environment of the university as “demanding but fair”, that their integration into the social environment happened in a later timeframe than their non-transfer counterparts, and that they were aware of the more “decentralized” campus services and department functions of the university when compared to CC. In particular, they reported the fast-paced progression of courses demonstrated the university’s high academic standards, and that they were inclined towards seeking informal resources (e.g., peers) as support rather than formal channels (e.g., faculty) [ 28 ]. Later, after interviewing 57 students, Owens [ 27 ] concluded that the TS had expected the academic atmosphere of the university to be more sophisticated, yet still feeling “intimidated” by the workload. They found it difficult to focus on their studies if they joined extracurricular activities and were concerned they were not “fitting into” the university culture, thus feeling “overwhelmed” and “completely alone”. In addition to academic and social aspects, Owens [ 27 ] also suggested an identity-related issue, with TS having a feeling of “marginality” in their university classes. Townsend and Wilson [ 29 ] conducted interviews with 19 students and suggested that TS were more interested than non-TS in the academic and social activities that would be helpful for their degree attainment. Another interview-based study with 11 TS also showed their emphasis on academic engagement and indifference towards building social networks [ 25 ].

Most of the studies that examined TS’ experiences have been conducted in Western educational contexts, predominately in the U.S., with small samples, and within limited majors. As well, most were conducted more than ten years ago. Furthermore, it is not uncommon for TS in Western countries to be more mature [ 30 ], racially or ethnically more diverse [ 31 ], and having had some full-time work experience prior to baccalaureate studies [ 32 ]. Despite being much less researched, CC transfer also takes place in such Asian countries and regions as mainland China, Hong Kong, and Malaysia [ 33 , 34 ]. Notably, the landscape of higher education in Asia might differ in that there is relatively less emphasis on racial diversity [ 35 ], and more on being “elite institutions” [ 36 ]. Academic qualifications and post-secondary employability seem to be more emphasized and pursued in Asian societies than in the West [ 37 ]. While it is increasingly common to earn post-secondary credentials (e.g., Associate Degrees) in Greater China [ 38 ], in Hong Kong these qualifications are mandatory requirements for CC graduates to transfer to university [ 33 ]. In particular, while CC TS in the West can normally spend several years of studies for obtaining their baccalaureate degrees, those in Hong Kong are normally required to complete their studies in two years [ 33 ]. Furthermore, in Hong Kong CC are largely perceived as the stepping stone for entering university [ 39 , 40 ], as opposed to CC in other parts of the world that primarily aim at democratizing and improving the affordability of higher education [ 41 , 42 ]. Pioneering quantitative research on TS in the Asian educational context, the authors [ 5 , 6 , 7 ] have conducted an institution-wide questionnaire survey in the university with the largest number of TS in Hong Kong, yielding that TS tend to experience obstacles with credit transfer, to experience transfer shock, to have heavier study loads and mental health issues, to be less active in participating in social and extra-curricular activities, and to show self-stigmatization. However, not much is known about the current learning experiences and challenges of TS across various disciplines. Moreover, compared to prior studies in the West [ 25 , 43 ], there is a lack of discussion on transfer pathways and articulation policies in the Asian education context, which can be reflected from delving into CC TS’ first-hand experience. Thus, we put forward the research question: In an Asian educational context, what are the challenges in CC TS’ experiences in university? The results will contribute to the TS literature, and also inform local and global stakeholders about institutional policies and practices, including senior management, student affairs offices, and academic advisors to tackle the transition issues and thus enhance the psychological well-being of the TS. As Asian students make up a considerable population in higher education all over the world, this study can also inform those receiving institutions of practices in supporting Asian TS as the target student population.

This study adopted a qualitative approach using focus group with TS on a designated topic (i.e., learning experiences and challenges in university study) in an informal setting [ 44 ]. Focus groups were chosen because they generate conversations that uncover individual opinions and reveal group consensus on their experience. They facilitate the interviewees to discuss potentially complex phenomena in a more amenable setting and reveal what they thought and why they thought what they did [ 45 ].

2.1. Research Context

Under normal circumstances, high school leavers who fail to meet the university entrance requirements on the public examination can enroll in local CCs that provide an “alternative route to higher education” (p. 147) [ 46 ]. Different from CC transfer in Western countries, a CC graduate in Hong Kong not only needs a competitive GPA, but also to obtain an associate degree (AD) or higher diploma (HD) in order to articulate vertically to bachelor’s degree studies [ 47 ]. These CC TS are often admitted to university as third-year students, and they are normally required to complete their baccalaureate studies full-time in two years (i.e., third and fourth years of the university).

2.2. Participants

Purposive sampling was used to recruit participants from a university in Hong Kong. The inclusion criteria were TS who had (a) graduated from a two-year CC, and (b) been studying in the bachelor’s degree program for at least one semester, or (c) graduated from the program within one or two years. The research assistant sent invitation emails to target participants through the university’s internal email system and invited those who replied to the emails to join the interview. Of the 124 interviewees, 84 (67.7%) were female. Sixty-one (49.2%) were in their first year of study, 45 (36.3%) in their second year, 9 (7.3%) in their third year, and 9 (7.3%) were graduates. Following the categorization of disciplines according to the Qualifications Framework ( http://www.hkqr.gov.hk/HKQRPRD/web/hkqr-en (accessed on 19 March 2021), 45 (36%) were from Medicine, Dentistry and Health Sciences (MDHS); 19 (15%) from Business and Management (BM); 17 (14%) from Services (SV); 11 (9%) from Architecture and Town Planning (ATP); 11 (9%) from Engineering and Technology (ET); 7 (6%) from Languages and Related Studies (LRS); 6 (5%) from Sciences (SS); 5 (4%) from Arts, Design and Performing Arts (ADPA); and 3 (2%) from Computer Science and Information Technology (CSIT).

2.3. Data Collection

Ethical approval was obtained (HSEARS20170808003) prior to conducting the interviews. The focus groups were conducted in a quiet room in the university and moderated by an interviewer with substantial experience in qualitative research. In addition to the interviewer, a research assistant joined each interview and noted the interactions among the participants. Students from the same program were interviewed as a group.

All focus groups were conducted in Cantonese, the commonly used language in Hong Kong after obtaining informed consent from each interviewee. An interview guide was developed ( Table 1 ). A funnel-based approach [ 48 ] was adopted; the interviews started with the broad question “Can you tell me about your experience studying in the university till now?” followed by probing questions that allowed the interviewees to elaborate on their experiences and challenges. The interviewer facilitated the discussion on the topic, which was essential for themes from the interviewees’ opinions to emerge during data collection and analysis [ 49 ]. After each interview, the interviewer and the research assistant discussed the key themes, hunches, interpretation, and what was new to the “data pool” and made minor adjustments to the interview questions whenever necessary.

Interview guide.

Owing to availability of the students, seven individual interviews were conducted in the university by the same interviewer using the focus groups guide. The funnel-based approach [ 48 ] was adopted. Each student was asked to share their experience in studying in university at the beginning of the interview. The interviewer then asked probing questions through which the students elaborated more on their transition experience, learning methods, and experience with other students. Interviews were completed when the students do not have any further comments to add.

2.4. Data Analysis

The audio recordings of the interviews were transcribed verbatim, word-by-word, in Chinese. The data were then imported into NVivo Pro 12 and coded for subsequent analysis using the inductive approach. Manifest qualitative content analysis facilitated us to have a broad and in-depth understanding of the phenomenon in question [ 50 ]. According to Graneheim and Lundman [ 51 ], qualitative content analysis consists of five steps: (i) read and re-read the interview transcriptions, (ii) identify the meaning units (sentences/ paragraphs) corresponding to different aspects of the students’ experiences, (iii) condense meaning units (i.e., excerpts) and label with codes, (iv) identify subcategories by comparing their similarities and differences, and (v) delineate the key categories. Through an iterative coding process where refinements were made from multiple rounds of going through the data, a coding framework was developed ( Table 2 ). The interviewer and the research assistant served as two independent coders. First, they coded the excerpts from the first 10 interviews (21.7% of 46 interviews) separately. They then discussed obtaining a consensus on the coding framework before adopting it to analyze the subsequent interviews, which involved two more research assistants. Intermediate summarizing and peer debriefings were conducted throughout the data analysis to ensure the consistency of the data analysis. Updates of the coding framework were made to match the evolving understanding of the phenomenon. The trustworthiness of the study was established through an audit trail that contained the raw data, field notes, intermediate summaries, process notes, and coding frameworks [ 52 ]. The strategies to ensure the trustworthiness of the findings have been described in other papers [ 8 , 9 ].

Categories and codes of the interview data.

Thirty-nine focus groups and seven individual interviews, depending on their availability, were conducted with 124 TSs from various disciplines and years of study from February 2018 to March 2019. The interviews consisted of one to five interviewees and lasted for 90–150 min. The categories and codes are summarized in Table 2 . Overall, the excerpts can be categorized as those related to the challenges with learning, issues of self-identity, and social experience. At the end of each quote presented in this paper, the interview identifier (#1 representing Interview 1), the interviewee identifier (e.g., S1 representing Interviewee 1), the discipline of interviewees (e.g., SV for Services, as shown above), and the interviewee’s year of study (e.g., Year 1 indicating first year) are displayed.

3.1. Challenges in Learning Experiences

As competition for admission to university is keen, the TS studied hard in CCs to aim for good academic performances. Some TS continued with the same mode of learning even after transferring to university, thus being motivated in their academic pursuits.

We were very serious when studying in the associate degree program. Some of us may have kept on with this mode of learning till now [in university]. (#33, S91, SV, Year 1)

We pay much more effort in studying than non-TS. We know very well what we want to achieve… We have been used to fighting for a high GPA. We try our best to study and have an in-depth understanding [of knowledge] and mastery of the practical skills. (#30, S80, ET, Year 1)

3.1.1. Heavy Workload

Perception of foreshortened period of bachelor study. With some credits transferred from the CC to their degree programs, TS’ studies can be shortened from four to an average of two to three years [ 33 ]. Logically, completing the study in a shorter period of time is an advantage of the transfer study pattern. However, because of the perceived foreshortened period of baccalaureate studies, the TS anticipated the need to catch up and had to deal with packed schedules.

The non-TS are at an advantage as they have more time. They study two more years [than us] in the university. (#2, S7, MDHS, Year 2)

We are different from non-TS. We focus more on academic performance. Studying is our priority. Actually, we are just like chasing an outstanding debt. That is, we have to catch up with the course content. The non-TS are more relaxed [than us] and they can manage both study and other activities. (#28, S76, MDHS, Graduate)

The TS generally reported having heavy study loads. They felt unsure if they would achieve better academic performances despite devoting most of their time and effort to this pursuit. They did not feel any sense of control over their own study, but needed to manage their study-induced stress.

After studying hard for two years in CC, I want to relax a bit when studying at university and graduate with an acceptable academic performance … We seize the time to study hard in university. However, we can no longer finish all the tasks like we did in [CC]. (#4, S14, MDHS, Year 2)

We need to manage the stress from studying because the workload is relatively high. Moreover, I expect to achieve a good academic performance. It is necessary to find myself some ways to learn better. (#3, S8, SV, Year 2)

Limited by insufficient credits transferred and extra workload from non-major courses. The majority of the students expressed that the graduation requirements for general education courses, along with service learning, contributed to heavy workloads. This was especially the case for students who had few credits transferred. They also pointed out having to take courses similar to those from their CC studies, in order to fulfil the graduation requirements.

I was only able to transfer the credits of two courses [from the CC to degree programmes]. As a result, I have to take six courses in each semester. I was basically studying all the time in my first year in university. It affected me a lot as I had no time for other activities. (#25, S71, MB, Year 2)

There were similar courses [between the CC and university], yet we failed to have the credits transferred. Then, we had to take those courses again. The contents were almost the same… It’s a waste of time. (#3, S9, SV, Year 2)

Due to their heavy workloads, the students suggested having credit transfer for more non-major courses.

The biggest challenge for me was fulfilling the requirements of the general education courses and service learning. (#1, S1, MDHS, Year 2)

The requirements for language and general education courses may be different in various institutions. Students may be required take these courses again even if they studied similar courses in CC. Their workloads could then be much heavier. I think having to take the courses related to our major is fine. Perhaps we could “waive” more for courses unrelated to the major. (#32, S87, ATP, Year 2)

Inflexible arrangement of time because of timetabling. The students commented that they have difficulty managing their time because of timetabling. Most of the courses for the TS were pre-assigned by their departments. In addition, due to limited vacancies in some courses and inflexible class timeslots, students might not always be able to enroll in their desired courses or classes. Subsequently, some of them had to give up taking some courses they were interested in or even gave up participating in overseas exchange programs.

Some courses are offered to the TS only. Therefore, there are limited choices of timeslots. We may waste the time in between lessons. The timetabling does not allow us to plan according to our needs and we do not take enough rest. (#46, S124, SS, Year 2)

TS face more restrictions in timetabling. We have to complete all requirements for general education courses within two years … Some students need to choose another service-learning course instead of the one they are interested in because of the time clashes. The purpose of facilitating personal development [in a university] was defeated. (#30, S83, ATP, Year 1)

Dearth of time for extracurricular activities. The majority of the students claimed that managing the time spent on different aspects was a formidable challenge. They had great expectations for active involvement in university life but reflected the reality that it was difficult to strike a balance between studying and extracurricular activities.

I want to maintain a balance between studying and engaging in extracurricular activities. I yearn to participate in some activities in the University. (#34, S92, ADPA, Year 1)

I wish to have memories of the university life that are more than just studying. (#34, S93, ADPA, Year 1)

Their (non-TS) time is more flexible, at least they have more chances to go abroad and to join activities. We cannot join even if we wish to do so because our workloads are too heavy. (#38, S103, BM, Year 1)

As shown, the TS in this study often compared themselves with the non-TS. Unlike the non-TS who had lighter workloads overall and more time to engage in a wider variety of campus activities, the TS felt that they were restricted from participating in any extra-curricular activities owing to their heavy workloads and inflexible schedules.

3.1.2. Change of Learning Approaches

Need to develop active and independent learning. Many TS deemed that the course content in university was more advanced, and the assessments were more difficult than their CC studies. In order to catch up, despite the difficulties, they needed to develop active and independent learning strategies and skills, such as self-directed learning.

The course content is not as straightforward as before….When we were studying in CC, we had a better [academic] performance after we studied harder for the examination. …Now [in university] it is not enough just to study hard … I need to learn by myself [without help from teachers]. When I can’t understand the content delivered in lectures, I need to depend on myself or discuss with classmates. (#37, S100, BM, Year 1)

We need to be self-directed in learning. It is not enough to spend more time reading. It is necessary to search for information and ask questions. (#1, S1, MDHS, Year 2)

Need to develop deep learning approach. The TS emphasized the need to demonstrate deep learning through understanding and applying the knowledge and practice.

We have to do self-learning in a lot of courses. Skills are taught in laboratory sessions. We have to spend much more time to practise before we can master the skills. … Some knowledge can’t be internalized without understanding. (#1, S4, MDHS, Year 2)

I had to demonstrate critical thinking when studying some courses. …I couldn’t handle assessments using rote learning only. (#35, S94, BM, Year 1)

We are not deadline fighters. We will find ways to understand the subject content after each class to avoid accumulating the workload till the end of the semester. (#27, S74, BM, Year 1)

The process of transitioning from a passive to an active learner showed that the TS had the need, the ability and, to some extent, the flexibility to adapt to alternative learning approaches.

3.2. Challenges in Self-Identity and Social Experience

Studying in university is the goal of all the graduates from CC. The social environment plays a significant role in adjustment of TSs but at the same time it also brings challenges which they might not anticipate.

3.2.1. Identifying Self with Reduced Sense of Competence

Sense of incompetence when compared with non-TS. The TS in this study achieved outstanding academic performances in their in CCs that allowed them to transfer to the university. However, it was common for them to express a sense of incompetence when comparing themselves with non-TS especially in terms of academic performance.

We need to have a good foundation in order to have improvement. During our first year of study in the degree programme, we attended classes together with non-TS for the upper division courses. … so some weaker TS might have found it hard to catch up. (#32, S87, ATP, Year 2)

Since we are TS, we might not be as clever as the non-TS. I am worried about the differences in our performance in assignments between us and the non-TS even though I put a lot of effort into my work. I feel inferior…. (#10, S31, SV, Year 1)

Lack of confidence and sense of inferiority. The TS appreciated the superior performance of non-TS in their in-class performances. They considered their own performances to be poorer and realized that this might be related to their lack of confidence and sense of inferiority.

I think we feel inferior so we believe they [non-TS] are better. I think we lack confidence … It is the reason for many issues … (#19, S59, MDHS, Year 3)

I couldn’t get a high GPA because of my low intelligence. I feel helpless. (#30, S84, ATP, Year 1)

They [non-TS] are much more confident than [us]... Even if they make mistakes, others might not notice them. I can see the difference. (#19, S59, MDHS, Year 3)

Uncomfortable when recognized as TS. Due to their sense of inferiority, the TS felt uncomfortable when recognized as TS in class. Some of them preferred to be integrated with non-TS in the learning environment. Despite their efforts, some believed they would not be able to integrate with non-TS.

Some lecturers check the attendance of TS [only] during lectures. … They shouldn’t label us. We are not lazy nor ignoring our study. (#7, S26, MDHS, Year 2)

We don’t want to be labeled that we failed in the public examination [for university entrance] and had to study in CC. We made a lot of effort, which most people don’t realize. (#7, S23, MDHS, Year 2)

3.2.2. Restricted Social Circle

Staying within the circle of fellow TS. The majority of the TS expressed their tendency to stay within social circles with other TS as they had similar past experiences. They had limited interactions and reported having distant relationships with the non-TS.

TS share similar experiences of failure in public examinations and thus the need to do an associate degree programme [in the CC], and then fighting hard to get into university. These similar experiences make it easier for us to get along with each other. (#6, S22, CSIT, Year 1)

Non-TS have been studying [in the university] for two or three years; they have groups of friends already. It is normal that they enjoy staying with their friends. …We stick with our own transfer student groups as well, which is just normal. (#10, S30, SV, Year 1)

Having different goals and experience in their study when compared with the non-TS. The TS were accustomed to their degree aspirations shaping their mindsets and attitudes during their CC studies, and this was sustained in their degree studies. They believed that TS had different goals and experience in their university studies and were therefore unlikely to befriend each other:

Non-TS and we have different goals. Unlike us, they do not focus too much on academic outcomes. It is difficult for us to befriend them since our goals are different. (#35, S94, BM, Year 1)

Transfer and non-TS have different university experiences. Many of them [non-TS] join overseas exchange programs, but only a few TS can have this opportunity. They can do the placement for the work-integrated education course a number of times, but we can only do it once. They can join various [extra-curricular] activities, but we can only focus on our study. (#45, S121, SS, Year 2)

Limited chances to interact with non-TS. Although some TS reported being in the same classes as the non-TS, others had limited chances to meet their non-transfer classmates. This might also have contributed to their restricted social circles.

In our first year in university, we were required to take courses different from those taken by the non-TS. …Then we only had the chance to study with non-TS in our second [i.e., final] year. (#46, S123, SS, Year 2)

We are getting along well with other TS, but I don’t know any non-TS. Sometimes I meet some non-TS in one or two lectures, thus we do not have many chances to interact with one another. (#14, S45, MDHS, Year 1)

Comparison of TSs with their peers especially the non-TSs resulted in their sense of inferiority. Restricting their social circles and staying with fellow TSs enhanced their sense of comfort and positive feelings but may also hinder engagement in university life.

4. Discussion and Implications

In this study, transferring from two-year CCs to four-year universities brought about both academic and social challenges to CC TS. This corroborates previous quantitative studies of TS’ experiences in both Western [ 11 , 17 ] and Asian educational contexts [ 4 , 5 , 6 , 7 ].

4.1. Heavy Study Load Due to Limited Timeframe

The TS in this study perceived themselves as having heavy study loads, and this concern intensified when the timeframe for graduation was restricted. In order to graduate “on time”, they need to finish courses pre-assigned by their departments and sacrifice their interests in favor of courses that fit into their schedules; they might even need to give up campus activities and overseas exchange programs [ 9 ]. In other words, for the purpose of timely degree attainment, TS need to make choices about their course enrollments with a sense of urgency. This implies that the students might not always have the opportunity to realize their learning interests, which might be further detrimental to their learning experience on top of the heavy workload. This was, however, not a cause for complaint by the students in this study, perhaps owing to the emphasis on academic qualifications and post-secondary employability in Asian societies [ 37 ] that would reinforce their focus on their academic outcomes. Furthermore, while CC in the West aim at improving the affordability of tertiary-level schooling [ 41 ], those in Hong Kong are self-financed and the high tuition fees (than undergraduate tuition) can put considerable strain on the resources of students and/or their families [ 42 ]. The sooner they graduate and find jobs, the sooner they will benefit from this investment in academic qualifications [ 53 ]. This pragmatic and instrumental perception of university education, which has been prevalent in Hong Kong for two decades [ 54 ], justifies TS’ adaptive strategies and compliance to the post-secondary system and curriculum.

Despite that TS in the U.S. also view the completion of a degree programs as “opportunities for promotion and increased income, changing life situations” [ 55 ], they are not required to attain their degrees in two or three years but can instead take several years [ 56 ]. TS in Western educational contexts are sometimes referred to as “non-traditional students” who are more mature and enrolled part-time [ 30 , 57 ]. Meanwhile, even though those in Hong Kong study full-time and are required to graduate in a timeframe similar to that of a third-year non-transfer student, their heavier workloads and, as discussed later, their poor self-identities imply that this “alternative route to higher education” (p. 147) [ 46 ] merits rethinking and modification with regard to its positioning in the higher education system in Hong Kong in the long run. Meanwhile, in the short run, administrators and curriculum coordinators from CC and universities should work together towards solutions (e.g., setting up articulation agreements for improving credit transfer) that help reduce TS’ study loads and thereby alleviate their study-induced stress, and thus enhance their psychological well-being.

4.2. Flexibility in Learning Approaches

The academic achievement in CC supported these TS to be admitted to universities [ 46 ], which can be viewed as a TS capital [ 16 ]. However, due to their strong emphasis on academic performance “inherited” from their CC studies, these TS were aware of the need to develop alternative learning approaches. Our study showed that students had the need and ability to evolve from passive to active and independent learners. This indicates that these TS were not restrained by their previous training in CC, but could instead capitalize on their prior experience and demonstrated flexibility in adapting to the more dynamic university learning environment [ 58 ]. Aside from learning approaches, the TS in this study also exhibited appropriate learning attitudes, as evidenced by the perception that being “deadline fighters” is an improper practice. In order not to accumulate the workload until the end of an already overloaded semester, they preferred an understanding of the knowledge delivered after each class rather than rote-memorizing all the content at once. This “learning by understanding” tendency can be regarded as a form of deep learning [ 59 ]. The ability to adjust their conceptions of how learning should take place and their corresponding actions can counterargue negative opinions about CC TS being academically underprepared [ 22 ].

4.3. Poor Self-Identity throughout Transfer Experience

The TS’ poor self-identity was possibly linked, first, to their undesirable experiences of not fulfilling the requirements of the university entrance examination, and second, to having to enroll in CC to grab the “second chance” of getting into university [ 40 ]. Wong [ 40 , 60 ] found that, prior to transferring to university, CC students in Hong Kong already perceived themselves as “losers”, and that they were likely to carry this label with them even when they eventually gained admission to university. This self-perception of failure might have been internalized and turned into the sense of inferiority and incompetence, especially when compared to non-TS. Nonetheless, this study has shown that these TS still managed to handle the multiple sources of workload and even demonstrated the abilities to develop active and independent learning while aiming for timely degree attainment. Regardless of their academic achievement, they seemed to have magnified their negative pre-transfer experiences and ignored that they were successful CC graduates who were retained in the highly competitive environment, still persisting with their baccalaureate studies [ 61 ]. TS’ self-stigmatization, as indicated by our findings, show that intervention is needed to rectify their self-perceptions. This implies that various stakeholders in the university, including student affairs officers, teachers, and advisors, should constantly remind TS of their excellence, on such occasions as admission, staff–student consultative meetings, and informal gatherings.

4.4. Lack of Social Involvement

TS are often not as active in their social involvement as their non-transfer counterparts [ 13 , 62 ]. In Western educational contexts, their lack of social involvement is usually attributed to their personal backgrounds including being from low-income families [ 63 ] or having family and work responsibilities [ 20 ]. In Asian educational contexts, especially those dominated by Chinese societal values (e.g., Hong Kong), high school leavers are expected to start their baccalaureate education immediately before becoming part of the workforce [ 64 ]. Upon completing two years of CC education, the average age of TS in Hong Kong is therefore comparable to that of third-year non-TS; this similar age range should have allowed integration between the two groups of students as intellectual peers [ 65 ]. Nonetheless, the TS in this study were still inclined to interact only with other TS. On one hand, these qualitative findings further explain our quantitative results [ 4 , 5 , 6 , 7 ]—their self-stigmatizing sense of incompetence and inferiority already prevented them from reaching out to other students whom they deemed “stronger” but were in fact not necessarily so. This implies that more educational and psychological supports are needed to inform TS about the empirical evidence on how the academic performances of the two student subpopulations have been shown to be statistically similar [ 66 , 67 ]. On the other hand, as revealed by the perception of “goals and experience”, they might have stereotyped non-TS as caring less about their academic pursuits but more about their extra-curriculars. This could be a misunderstanding that has not been communicated between the two student groups, as some undergraduate students might still prioritize academic performance over other commitments [ 43 ]. With the absence of interactions between transfer and non-TS, the intervention of external parties such as administrators and teachers will play a vital role in helping to blend the two groups together. First, on the level of course arrangements, departments can arrange classes containing diverse student demographics, shown to facilitate teaching and learning in higher education [ 68 ]. Second, given that peer support is as important as teacher support [ 12 ], academic advisors should prompt non-TS to take the initiative in socializing with and assisting TS in both their academic and social integration. Last but not least, lecturers and tutors should avoid verbally expressing any labelling of TS, especially during classes and meetings [ 21 ].

4.5. Limitations

The current study adopted purposive sampling and a qualitative research design with a large sample size of 124 from nine disciplines. However, our sample was from a single institution, which hosts the most of TS in Hong Kong. To yield a more comprehensive picture of TS’ experience in Hong Kong, ongoing work is being conducted, analyzing interview data from other local institutions. Group interviews may hinder interviewees to reveal specific personal issues. In the study, seven individual interviews were conducted because of the availability of the interviewees. They allowed a more in-depth exploration of individual experience, but sharing among interviewees was not possible. This study aimed to synthesize the experience and challenges of TS in Hong Kong as one of the Asian educational contexts. The socio-cultural influence on their coping with the challenges and the required support deserve further exploration.

5. Conclusions

In this study, we explored the experiences of TS in an Asian educational context, which have received little attention in previous studies. The alternative pathway of TS entailed a different experience in their university studies when compared with non-TS. In the academic aspect, they faced a heavy workload from having to complete their studies within a limited timeframe for degree attainment with study-induced stress and had to develop learning approaches different from those used in CCs. In terms of their self-identity, they compared themselves with non-TS and felt less competent and inferior. Regarding their social experiences, they tended to bond with other TS and had limited chances of interacting with non-TS. As discussed, future policies and practices are warranted, not only to improve TS’ academic and social integration, but also to improve their self-perceptions and psychological well-being.

Acknowledgments

The authors would like to acknowledge the funding provided by University Grant Committee (UGC) Funding Scheme for Teaching and Learning Related Proposals (2016-19 Triennium) (PolyU6/T&L/16-19). Thanks also go to Gwendoline Yuanyuan Guan for her help. The authors are grateful to all the college transfer students for their participation in the study.

Author Contributions

Conceptualization, K.C. and S.S.Y.C.; methodology, K.C. and S.S.Y.C.; software, L.W.Z.; validation, K.Y.T. and J.N.; formal analysis, L.W.Z.; in-vestigation, L.W.Z. and S.S.Y.C.; resources, K.C.; data curation, K.C.; writing—original draft preparation, L.W.Z. and K.Y.T.; writing—review and editing, J.N., S.S.Y.C., K.Y.T. and K.C.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.

The study was funded by the University Grant Committee (UGC) Funding Scheme for Teaching and Learning Related Proposals (2016-19 Triennium) (PolyU6/T&L/16-19). The funder did not have any role in the design, methods, analysis, or preparation of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki. Ethical approval was obtained from the Human Subjects Ethics Sub-Committee of the Hong Kong Polytechnic University (HSEARS20170808003).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Roughly one-in-five teenagers who have heard of ChatGPT say they have used it to help them do their schoolwork, according to a new Pew Research Center survey of U.S. teens ages 13 to 17. With a majority of teens having heard of ChatGPT, that amounts to 13% of all U.S. teens who have used the generative artificial intelligence (AI) chatbot in their schoolwork.

A bar chart showing that, among teens who know of ChatGPT, 19% say they’ve used it for schoolwork.

Teens in higher grade levels are particularly likely to have used the chatbot to help them with schoolwork. About one-quarter of 11th and 12th graders who have heard of ChatGPT say they have done this. This share drops to 17% among 9th and 10th graders and 12% among 7th and 8th graders.

There is no significant difference between teen boys and girls who have used ChatGPT in this way.

The introduction of ChatGPT last year has led to much discussion about its role in schools , especially whether schools should integrate the new technology into the classroom or ban it .

Pew Research Center conducted this analysis to understand American teens’ use and understanding of ChatGPT in the school setting.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, via Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions used for this analysis , along with responses, and its methodology .

Teens’ awareness of ChatGPT

Overall, two-thirds of U.S. teens say they have heard of ChatGPT, including 23% who have heard a lot about it. But awareness varies by race and ethnicity, as well as by household income:

A horizontal stacked bar chart showing that most teens have heard of ChatGPT, but awareness varies by race and ethnicity, household income.

72% of White teens say they’ve heard at least a little about ChatGPT, compared with 63% of Hispanic teens and 56% of Black teens.
75% of teens living in households that make $75,000 or more annually have heard of ChatGPT. Much smaller shares in households with incomes between $30,000 and $74,999 (58%) and less than $30,000 (41%) say the same.

Teens who are more aware of ChatGPT are more likely to use it for schoolwork. Roughly a third of teens who have heard a lot about ChatGPT (36%) have used it for schoolwork, far higher than the 10% among those who have heard a little about it.

When do teens think it’s OK for students to use ChatGPT?

For teens, whether it is – or is not – acceptable for students to use ChatGPT depends on what it is being used for.

There is a fair amount of support for using the chatbot to explore a topic. Roughly seven-in-ten teens who have heard of ChatGPT say it’s acceptable to use when they are researching something new, while 13% say it is not acceptable.

A diverging bar chart showing that many teens say it’s acceptable to use ChatGPT for research; few say it’s OK to use it for writing essays.

However, there is much less support for using ChatGPT to do the work itself. Just one-in-five teens who have heard of ChatGPT say it’s acceptable to use it to write essays, while 57% say it is not acceptable. And 39% say it’s acceptable to use ChatGPT to solve math problems, while a similar share of teens (36%) say it’s not acceptable.

Some teens are uncertain about whether it’s acceptable to use ChatGPT for these tasks. Between 18% and 24% say they aren’t sure whether these are acceptable use cases for ChatGPT.

Those who have heard a lot about ChatGPT are more likely than those who have only heard a little about it to say it’s acceptable to use the chatbot to research topics, solve math problems and write essays. For instance, 54% of teens who have heard a lot about ChatGPT say it’s acceptable to use it to solve math problems, compared with 32% among those who have heard a little about it.

Note: Here are the questions used for this analysis , along with responses, and its methodology .

Artificial Intelligence
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Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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A Qualitative Exploration on the Challenges of Transfer Students in an Asian Educational Context

Kin Yuen Tam

Lillian Weiwei Zhang

1. Introduction

2.1. Research Context

2.2. Participants

2.3. Data Collection

2.4. Data Analysis

3.1. Challenges in Learning Experiences

3.1.1. Heavy Workload

3.1.2. Change of Learning Approaches

3.2. Challenges in Self-Identity and Social Experience

3.2.1. Identifying Self with Reduced Sense of Competence

3.2.2. Restricted Social Circle

4. Discussion and Implications

4.1. Heavy Study Load Due to Limited Timeframe

4.2. Flexibility in Learning Approaches

4.3. Poor Self-Identity throughout Transfer Experience

4.4. Lack of Social Involvement

4.5. Limitations

5. Conclusions

Acknowledgments

Author Contributions

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

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About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Teens’ awareness of ChatGPT

When do teens think it’s OK for students to use ChatGPT?

Many Americans think generative AI programs should credit the sources they rely on

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